El DataFrame en cuestión está formado por las características extraídas de un array de datos al comprimirlo y descomprimirlo mediante blosc. En cada fichero aparecen distintos conjuntos de datos los cuáles dividimos en fragmentos de 16 MegaBytes y sobre los cuales realizamos las pruebas de compresión y decompresión.
Cada fila se corresponde con los datos de realizar los test de compresión sobre un fragmento (chunk) de datos específico con un tamaño de bloque, codec, filtro y nivel de compresión determinados.
| Variable | Descripción |
|---|---|
| Filename | nombre del fichero del que proviene. |
| DataSet | dentro del fichero el conjunto de datos del que proviene. |
| Table | 0 si los datos vienen de un array, 1 si vienen de tablas y 2 para tablas columnares. |
| DType | indica el tipo de los datos. |
| Chunk_Number | número de fragmento dentro del conjunto de datos. |
| Chunk_Size | tamaño del fragmento. |
| Mean | la media. |
| Median | la mediana. |
| Sd | la desviación típica. |
| Skew | el coeficiente de asimetría. |
| Kurt | el coeficiente de apuntamiento. |
| Min | el mínimo absoluto. |
| Max | el máximo absoluto. |
| Q1 | el primer cuartil. |
| Q3 | el tercer cuartil. |
| N_Streaks | número de rachas seguidas por encima o debajo de la mediana. |
| Block_Size | el tamaño de bloque que utilizará Blosc para comprimir. |
| Codec | el codec de blosc utilizado. |
| Filter | el filtro de blosc utilizado. |
| CL | el nivel de compresión utilizado. |
| CRate | el ratio de compresión obtenido. |
| CSpeed | la velocidad de compresión obtenida en GB/s. |
| DSpeed | la velocidad de decompresión obtenida en GB/s. |
%matplotlib inline
%config InlineBackend.figure_format='retina'
%load_ext autoreload
%autoreload 2
%load_ext version_information
%version_information numpy, scipy, matplotlib, pandas
| Software | Version |
|---|---|
| Python | 3.5.3 64bit [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] |
| IPython | 5.1.0 |
| OS | Linux 4.9.16 gentoo x86_64 with debian stretch sid |
| numpy | 1.12.1 |
| scipy | 0.19.0 |
| matplotlib | 2.0.0 |
| pandas | 0.19.2 |
| Sat Apr 29 10:46:42 2017 UTC | |
import os
import sys
sys.path.append("../src/")
from IPython.display import display
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
import custom_plots as cst
pd.options.display.float_format = '{:,.3f}'.format
matplotlib.rcParams.update({'font.size': 12})
Cargamos el csv entero, comprobamos que no faltan campos y mostramos un breve resumen.
# LOAD WHOLE CSV
my_df = pd.read_csv('../data/blosc_test_data_final.csv.gz', sep='\t')
# SORT COLUMNS
my_df = my_df[cst.COLS]
# CHECK MISSING DATA
if not my_df.isnull().any().any():
print('No missing data')
else:
print("Missing data")
No missing data
# SUMMARY OF THE DATAFRAME
display(my_df[cst.COLS[5:]].describe())
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | Block_Size | CL | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 | 1,127,520.000 |
| mean | 14.384 | 5,855,203,352,860,118.000 | 5,855,510,553,455,461.000 | 3,338,034,656,223.127 | 12.276 | 2,960.293 | 5,849,131,049,179,057.000 | 5,860,675,864,112,030.000 | 5,852,583,057,632,408.000 | 5,858,072,925,864,367.000 | 279,772.803 | 408.800 | 5.000 | 83.863 | 3.324 | 6.751 |
| std | 3.965 | 88,991,425,790,313,680.000 | 88,996,095,055,340,192.000 | 50,744,320,308,850.523 | 40.009 | 20,641.213 | 88,899,134,721,544,352.000 | 89,074,600,722,058,176.000 | 88,951,600,897,789,536.000 | 89,035,039,572,418,704.000 | 447,871.918 | 626.196 | 2.582 | 618.729 | 4.209 | 4.595 |
| min | 0.015 | -996.946 | -999.000 | 0.000 | -134.250 | -3.000 | -999.000 | -4.000 | -999.000 | -999.000 | 0.000 | 0.000 | 1.000 | 0.999 | 0.001 | 0.179 |
| 25% | 16.000 | 0.000 | 0.000 | 0.183 | 0.062 | -0.888 | -14.210 | 9.489 | 0.000 | 0.000 | 18,130.000 | 16.000 | 3.000 | 1.870 | 0.388 | 3.113 |
| 50% | 16.000 | 0.101 | 0.000 | 2.679 | 3.052 | 12.431 | 0.000 | 32.387 | 0.000 | 0.077 | 125,503.000 | 96.000 | 5.000 | 4.932 | 1.707 | 6.081 |
| 75% | 16.000 | 3.121 | 0.001 | 59.326 | 9.994 | 184.219 | 0.000 | 133.042 | 0.000 | 18.980 | 410,675.000 | 512.000 | 7.000 | 17.533 | 4.566 | 9.364 |
| max | 16.000 | 1,358,459,542,578,043,904.000 | 1,358,622,091,149,467,648.000 | 795,493,396,001,273.125 | 497.825 | 316,831.759 | 1,356,998,404,761,455,616.000 | 1,359,676,799,663,922,944.000 | 1,357,915,976,235,253,760.000 | 1,359,154,499,978,547,968.000 | 4,367,308.000 | 2,048.000 | 9.000 | 10,645.442 | 23.848 | 86.345 |
Como se puede observar hay mucha variabilidad en nuestros datos, lo cual es bueno.
Veamos cuantos conjuntos de datos tiene el fichero.
sets = my_df.drop_duplicates(subset=['DataSet', 'Table'])[cst.DESC_SET]
print('First ten datasets')
display(sets.head(n=10))
print('There are %d datasets' % (sets.shape[0]))
First ten datasets
| DataSet | DType | Table | Chunk_Size | |
|---|---|---|---|---|
| 0 | /U | float32 | 0.000 | 16.000 |
| 85860 | /V | float32 | 0.000 | 16.000 |
| 150660 | /Grids/G1/precipAllObs | int32 | 0.000 | 0.738 |
| 152280 | /Grids/G1/surfPrecipLiqRateProb | float32 | 0.000 | 0.015 |
| 153900 | /Grids/G1/surfPrecipLiqRateUn | float32 | 0.000 | 0.015 |
| 155520 | /Grids/G1/surfPrecipTotRateDiurnalAllObs | int32 | 0.000 | 1.107 |
| 157140 | /Grids/G1/surfPrecipTotRateProb | float32 | 0.000 | 0.015 |
| 158760 | /Grids/G1/surfPrecipTotRateUn | float32 | 0.000 | 0.015 |
| 160380 | /Grids/G2/precipAllObs | int32 | 0.000 | 16.000 |
| 170100 | /Grids/G2/surfPrecipLiqRateProb | float32 | 0.000 | 5.889 |
There are 120 datasets
Procedemos a mostrar un resumen de las características extraídas de cada conjunto de datos.
for dataset in sets.drop_duplicates(subset=['DataSet'])['DataSet']:
set_info = sets[sets.DataSet == dataset]
print('SUMMARY')
print(set_info)
aux_set = my_df[my_df.DataSet == dataset].drop_duplicates(subset=['Chunk_Number'])
if aux_set.shape[0] > 1:
display(aux_set.describe()[cst.CHUNK_FEATURES])
else:
display(aux_set[cst.CHUNK_FEATURES])
SUMMARY DataSet DType Table Chunk_Size 0 /U float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 | 53.000 |
| mean | 15.726 | 14.314 | 9.838 | 12.731 | 0.624 | -0.720 | -15.663 | 48.531 | 4.747 | 24.449 | 94,648.358 |
| std | 1.995 | 4.350 | 3.881 | 2.527 | 0.153 | 0.319 | 4.249 | 9.016 | 3.546 | 6.457 | 19,730.974 |
| min | 1.475 | 5.762 | 2.096 | 6.404 | 0.228 | -1.164 | -28.273 | 27.146 | -1.446 | 11.317 | 13,756.000 |
| 25% | 16.000 | 10.708 | 7.198 | 11.469 | 0.532 | -0.921 | -17.664 | 43.313 | 1.943 | 18.781 | 82,271.000 |
| 50% | 16.000 | 15.430 | 9.564 | 13.268 | 0.649 | -0.793 | -14.891 | 50.507 | 4.873 | 25.905 | 95,832.000 |
| 75% | 16.000 | 17.302 | 13.019 | 14.752 | 0.738 | -0.624 | -12.637 | 53.064 | 7.273 | 27.890 | 109,934.000 |
| max | 16.000 | 22.909 | 16.944 | 17.010 | 1.072 | 0.549 | -9.488 | 62.922 | 10.356 | 38.366 | 124,896.000 |
SUMMARY
DataSet DType Table Chunk_Size
85860 /V float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 | 40.000 |
| mean | 15.900 | 2.073 | 1.526 | 4.976 | 0.311 | 0.301 | -16.644 | 23.037 | -1.197 | 4.881 | 194,369.825 |
| std | 0.631 | 2.517 | 1.696 | 2.391 | 0.510 | 0.742 | 4.047 | 9.672 | 1.472 | 3.369 | 36,781.843 |
| min | 12.009 | -1.626 | -1.256 | 2.474 | -0.600 | -0.370 | -29.640 | 13.443 | -4.887 | 0.552 | 141,024.000 |
| 25% | 16.000 | -0.240 | -0.088 | 3.414 | -0.031 | -0.162 | -19.447 | 16.708 | -2.397 | 2.357 | 162,933.000 |
| 50% | 16.000 | 1.886 | 1.721 | 4.303 | 0.253 | 0.240 | -16.237 | 20.683 | -0.907 | 4.500 | 186,578.000 |
| 75% | 16.000 | 3.743 | 2.851 | 5.930 | 0.644 | 0.484 | -13.344 | 25.571 | -0.204 | 6.422 | 219,685.000 |
| max | 16.000 | 9.091 | 4.968 | 14.419 | 1.748 | 4.040 | -11.483 | 56.161 | 1.253 | 15.373 | 293,423.000 |
SUMMARY
DataSet DType Table Chunk_Size
150660 /Grids/G1/precipAllObs int32 0.000 0.738
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 150660 | 0.738 | 46,750.635 | 42,412.000 | 42,964.463 | 1.123 | 2.123 | 0.000 | 211,383.000 | 121.000 | 79,434.750 | 27,744.000 |
SUMMARY
DataSet DType Table Chunk_Size
152280 /Grids/G1/surfPrecipLiqRateProb float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 152280 | 0.015 | 0.044 | 0.037 | 0.040 | 1.346 | 3.059 | 0.000 | 0.352 | 0.011 | 0.066 | 1,032.000 |
SUMMARY
DataSet DType Table Chunk_Size
153900 /Grids/G1/surfPrecipLiqRateUn float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 153900 | 0.015 | 0.092 | 0.048 | 0.123 | 2.762 | 12.094 | 0.000 | 1.414 | 0.011 | 0.124 | 992.000 |
SUMMARY
DataSet DType Table Chunk_Size
155520 /Grids/G1/surfPrecipTotRateDiurnalAllObs int32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 155520 | 1.107 | 1,947.943 | 272.000 | 2,888.095 | 2.804 | 13.277 | 0.000 | 24,063.000 | 0.000 | 3,094.000 | 31,604.000 |
SUMMARY
DataSet DType Table Chunk_Size
157140 /Grids/G1/surfPrecipTotRateProb float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 157140 | 0.015 | 0.050 | 0.043 | 0.040 | 1.218 | 2.721 | 0.000 | 0.352 | 0.018 | 0.072 | 1,137.000 |
SUMMARY
DataSet DType Table Chunk_Size
158760 /Grids/G1/surfPrecipTotRateUn float32 0.000 0.015
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 158760 | 0.015 | 0.101 | 0.064 | 0.121 | 2.739 | 12.272 | 0.000 | 1.414 | 0.022 | 0.133 | 1,099.000 |
SUMMARY
DataSet DType Table Chunk_Size
160380 /Grids/G2/precipAllObs int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 |
| mean | 15.703 | 183.354 | 173.500 | 107.464 | 2.317 | 9.421 | 0.000 | 910.000 | 116.167 | 222.333 | 198,604.333 |
| std | 0.727 | 6.775 | 10.710 | 1.604 | 0.023 | 0.173 | 0.000 | 0.000 | 5.742 | 4.502 | 14,750.195 |
| min | 14.219 | 176.954 | 163.000 | 105.856 | 2.292 | 9.218 | 0.000 | 910.000 | 111.000 | 218.000 | 179,642.000 |
| 25% | 16.000 | 177.709 | 164.250 | 106.084 | 2.296 | 9.273 | 0.000 | 910.000 | 111.500 | 218.500 | 188,721.000 |
| 50% | 16.000 | 181.741 | 171.500 | 107.211 | 2.316 | 9.426 | 0.000 | 910.000 | 114.500 | 221.500 | 197,790.500 |
| 75% | 16.000 | 187.916 | 181.750 | 108.644 | 2.338 | 9.577 | 0.000 | 910.000 | 119.750 | 225.250 | 210,105.250 |
| max | 16.000 | 193.347 | 188.000 | 109.676 | 2.342 | 9.605 | 0.000 | 910.000 | 125.000 | 229.000 | 216,495.000 |
SUMMARY
DataSet DType Table Chunk_Size
170100 /Grids/G2/surfPrecipLiqRateProb float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 170100 | 5.889 | 0.045 | 0.009 | 0.074 | 2.804 | 12.031 | 0.000 | 1.000 | 0.000 | 0.063 | 291,171.000 |
SUMMARY
DataSet DType Table Chunk_Size
171720 /Grids/G2/surfPrecipLiqRateUn float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 171720 | 5.889 | 0.094 | 0.004 | 0.337 | 12.404 | 321.944 | 0.000 | 26.186 | 0.000 | 0.051 | 288,953.000 |
SUMMARY
DataSet DType Table Chunk_Size
173340 /Grids/G2/surfPrecipTotRateDiurnalAllObs int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 7.629 | 0.000 | 12.705 | 1.798 | 4.317 | 0.000 | 102.222 | 0.000 | 13.778 | 154,746.778 |
| std | 0.891 | 1.237 | 0.000 | 0.843 | 0.792 | 4.134 | 0.000 | 17.683 | 0.000 | 8.059 | 56,353.353 |
| min | 13.328 | 5.467 | 0.000 | 11.541 | 0.728 | -1.091 | 0.000 | 65.000 | 0.000 | 0.000 | 80,589.000 |
| 25% | 16.000 | 7.239 | 0.000 | 11.730 | 1.005 | -0.012 | 0.000 | 93.000 | 0.000 | 14.000 | 109,781.000 |
| 50% | 16.000 | 8.051 | 0.000 | 12.935 | 1.725 | 4.011 | 0.000 | 113.000 | 0.000 | 18.000 | 161,634.000 |
| 75% | 16.000 | 8.505 | 0.000 | 13.343 | 2.259 | 6.763 | 0.000 | 113.000 | 0.000 | 19.000 | 207,840.000 |
| max | 16.000 | 9.073 | 0.000 | 13.710 | 2.875 | 9.907 | 0.000 | 114.000 | 0.000 | 20.000 | 223,100.000 |
SUMMARY
DataSet DType Table Chunk_Size
187920 /Grids/G2/surfPrecipTotRateProb float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 187920 | 5.889 | 0.050 | 0.017 | 0.075 | 2.606 | 10.682 | 0.000 | 1.000 | 0.000 | 0.074 | 305,495.000 |
SUMMARY
DataSet DType Table Chunk_Size
189540 /Grids/G2/surfPrecipTotRateUn float32 0.000 5.889
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 189540 | 5.889 | 0.103 | 0.011 | 0.338 | 12.253 | 317.113 | 0.000 | 26.186 | 0.000 | 0.074 | 304,781.000 |
SUMMARY
DataSet DType Table Chunk_Size
191160 /Grids/G1/precipLiqRate/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 191160 | 2.215 | 290.349 | 0.000 | 1,105.965 | 6.631 | 63.209 | 0.000 | 27,765.000 | 0.000 | 0.000 | 28,860.000 |
SUMMARY
DataSet DType Table Chunk_Size
192780 /Grids/G1/precipLiqRate/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 8.039 | 0.000 | 37.628 | 29.783 | 3,946.721 | 0.000 | 1,672.200 | 0.000 | 0.000 | 152,817.200 |
| std | 6.062 | 8.998 | 0.000 | 39.709 | 39.245 | 8,240.638 | 0.000 | 1,584.709 | 0.000 | 0.000 | 106,146.943 |
| min | 2.445 | 0.000 | 0.000 | 0.027 | 8.448 | 112.235 | 0.000 | 8.000 | 0.000 | 0.000 | 505.000 |
| 25% | 16.000 | 0.256 | 0.000 | 2.879 | 8.726 | 116.838 | 0.000 | 222.000 | 0.000 | 0.000 | 81,639.000 |
| 50% | 16.000 | 4.585 | 0.000 | 25.789 | 12.198 | 243.864 | 0.000 | 1,574.000 | 0.000 | 0.000 | 219,793.000 |
| 75% | 16.000 | 16.949 | 0.000 | 78.809 | 20.056 | 576.522 | 0.000 | 3,243.000 | 0.000 | 0.000 | 225,193.000 |
| max | 16.000 | 18.402 | 0.000 | 80.639 | 99.485 | 18,684.148 | 0.000 | 3,314.000 | 0.000 | 0.000 | 236,956.000 |
SUMMARY
DataSet DType Table Chunk_Size
200880 /Grids/G1/precipLiqRate/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 200880 | 2.215 | 0.456 | 0.000 | 1.532 | 8.146 | 210.120 | 0.000 | 122.311 | 0.000 | 0.000 | 28,860.000 |
SUMMARY
DataSet DType Table Chunk_Size
202500 /Grids/G1/precipLiqRate/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 202500 | 2.215 | 0.650 | 0.000 | 2.098 | 4.997 | 32.385 | 0.000 | 43.932 | 0.000 | 0.000 | 29,002.000 |
SUMMARY
DataSet DType Table Chunk_Size
204120 /Grids/G1/precipLiqWaterContent/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 204120 | 2.215 | 290.345 | 0.000 | 1,105.955 | 6.631 | 63.210 | 0.000 | 27,765.000 | 0.000 | 0.000 | 28,858.000 |
SUMMARY
DataSet DType Table Chunk_Size
205740 /Grids/G1/precipLiqWaterContent/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 8.039 | 0.000 | 37.307 | 19.295 | 988.704 | 0.000 | 1,682.600 | 0.000 | 0.000 | 166,725.600 |
| std | 6.062 | 8.647 | 0.000 | 37.812 | 17.888 | 1,688.395 | 0.000 | 1,473.851 | 0.000 | 0.000 | 100,350.938 |
| min | 2.445 | 0.002 | 0.000 | 0.055 | 8.180 | 105.888 | 0.000 | 8.000 | 0.000 | 0.000 | 1,523.000 |
| 25% | 16.000 | 0.505 | 0.000 | 4.463 | 9.521 | 136.409 | 0.000 | 534.000 | 0.000 | 0.000 | 141,605.000 |
| 50% | 16.000 | 5.707 | 0.000 | 28.381 | 10.514 | 180.766 | 0.000 | 1,499.000 | 0.000 | 0.000 | 222,958.000 |
| 75% | 16.000 | 14.534 | 0.000 | 71.785 | 17.644 | 526.645 | 0.000 | 3,111.000 | 0.000 | 0.000 | 223,621.000 |
| max | 16.000 | 19.445 | 0.000 | 81.850 | 50.616 | 3,993.813 | 0.000 | 3,261.000 | 0.000 | 0.000 | 243,921.000 |
SUMMARY
DataSet DType Table Chunk_Size
213840 /Grids/G1/precipLiqWaterContent/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 213840 | 2.215 | 0.036 | 0.000 | 0.104 | 5.367 | 54.662 | 0.000 | 4.711 | 0.000 | 0.000 | 28,858.000 |
SUMMARY
DataSet DType Table Chunk_Size
215460 /Grids/G1/precipLiqWaterContent/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 215460 | 2.215 | 0.044 | 0.000 | 0.127 | 4.180 | 23.454 | 0.000 | 3.249 | 0.000 | 0.000 | 29,004.000 |
SUMMARY
DataSet DType Table Chunk_Size
217080 /Grids/G1/precipTotDm/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 217080 | 2.215 | 448.096 | 0.000 | 1,326.224 | 5.267 | 40.686 | 0.000 | 28,569.000 | 0.000 | 133.000 | 71,620.000 |
SUMMARY
DataSet DType Table Chunk_Size
218700 /Grids/G1/precipTotDm/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.435 | 0.000 | 59.024 | 14.518 | 420.334 | 0.000 | 2,956.400 | 0.000 | 0.200 | 273,333.400 |
| std | 6.062 | 16.223 | 0.000 | 70.813 | 5.669 | 397.280 | 0.000 | 3,262.573 | 0.000 | 0.447 | 163,043.171 |
| min | 2.445 | 0.174 | 0.000 | 1.476 | 8.436 | 115.124 | 0.000 | 120.000 | 0.000 | 0.000 | 24,775.000 |
| 25% | 16.000 | 0.247 | 0.000 | 1.770 | 12.399 | 236.340 | 0.000 | 123.000 | 0.000 | 0.000 | 202,242.000 |
| 50% | 16.000 | 4.057 | 0.000 | 22.359 | 13.068 | 290.349 | 0.000 | 1,645.000 | 0.000 | 0.000 | 320,239.000 |
| 75% | 16.000 | 20.339 | 0.000 | 116.685 | 14.963 | 345.400 | 0.000 | 5,991.000 | 0.000 | 0.000 | 402,557.000 |
| max | 16.000 | 37.359 | 0.000 | 152.827 | 23.725 | 1,114.457 | 0.000 | 6,903.000 | 0.000 | 1.000 | 416,854.000 |
SUMMARY
DataSet DType Table Chunk_Size
226800 /Grids/G1/precipTotDm/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 226800 | 2.215 | 0.372 | 0.000 | 0.462 | 0.751 | -0.713 | 0.000 | 3.912 | 0.000 | 0.723 | 71,620.000 |
SUMMARY
DataSet DType Table Chunk_Size
228420 /Grids/G1/precipTotDm/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 228420 | 2.215 | 0.088 | 0.000 | 0.145 | 4.611 | 99.472 | 0.000 | 7.870 | 0.000 | 0.152 | 68,718.000 |
SUMMARY
DataSet DType Table Chunk_Size
230040 /Grids/G1/precipTotLogNw/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 230040 | 2.215 | 547.558 | 0.000 | 1,556.714 | 5.086 | 37.663 | 0.000 | 31,082.000 | 0.000 | 199.000 | 71,220.000 |
SUMMARY
DataSet DType Table Chunk_Size
231660 /Grids/G1/precipTotLogNw/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 15.174 | 0.000 | 84.957 | 12.545 | 341.127 | 0.000 | 6,379.400 | 0.000 | 0.800 | 208,872.600 |
| std | 6.062 | 23.427 | 0.000 | 113.093 | 7.418 | 223.816 | 0.000 | 8,125.515 | 0.000 | 1.789 | 227,324.151 |
| min | 2.445 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.086 | 0.000 | 0.652 | 11.657 | 244.801 | 0.000 | 53.000 | 0.000 | 0.000 | 30,499.000 |
| 50% | 16.000 | 2.822 | 0.000 | 18.682 | 16.203 | 426.538 | 0.000 | 1,327.000 | 0.000 | 0.000 | 111,381.000 |
| 75% | 16.000 | 18.085 | 0.000 | 154.231 | 16.794 | 484.369 | 0.000 | 14,965.000 | 0.000 | 0.000 | 405,029.000 |
| max | 16.000 | 54.877 | 0.000 | 251.221 | 18.071 | 552.928 | 0.000 | 15,552.000 | 0.000 | 4.000 | 497,453.000 |
SUMMARY
DataSet DType Table Chunk_Size
239760 /Grids/G1/precipTotLogNw/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 239760 | 2.215 | 3.392 | 0.000 | 3.697 | 0.191 | -1.926 | 0.000 | 9.957 | 0.000 | 7.310 | 71,220.000 |
SUMMARY
DataSet DType Table Chunk_Size
241380 /Grids/G1/precipTotLogNw/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 241380 | 2.215 | 0.129 | 0.000 | 0.174 | 1.084 | 0.046 | 0.000 | 1.303 | 0.000 | 0.246 | 68,300.000 |
SUMMARY
DataSet DType Table Chunk_Size
243000 /Grids/G1/precipTotRate/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 243000 | 2.215 | 448.460 | 0.000 | 1,326.900 | 5.265 | 40.637 | 0.000 | 28,569.000 | 0.000 | 134.000 | 71,708.000 |
SUMMARY
DataSet DType Table Chunk_Size
244620 /Grids/G1/precipTotRate/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.416 | 0.000 | 48.134 | 18.219 | 903.809 | 0.000 | 1,956.400 | 0.000 | 1.600 | 300,028.000 |
| std | 6.062 | 16.201 | 0.000 | 55.736 | 16.732 | 1,561.170 | 0.000 | 2,024.067 | 0.000 | 3.578 | 213,353.766 |
| min | 2.445 | 0.001 | 0.000 | 0.045 | 6.722 | 70.836 | 0.000 | 7.000 | 0.000 | 0.000 | 1,533.000 |
| 25% | 16.000 | 0.342 | 0.000 | 3.142 | 8.825 | 118.443 | 0.000 | 222.000 | 0.000 | 0.000 | 154,303.000 |
| 50% | 16.000 | 6.154 | 0.000 | 28.635 | 10.666 | 188.103 | 0.000 | 1,574.000 | 0.000 | 0.000 | 396,844.000 |
| 75% | 16.000 | 16.885 | 0.000 | 78.235 | 17.648 | 458.031 | 0.000 | 3,226.000 | 0.000 | 0.000 | 441,901.000 |
| max | 16.000 | 38.698 | 0.000 | 130.615 | 47.237 | 3,683.632 | 0.000 | 4,753.000 | 0.000 | 8.000 | 505,559.000 |
SUMMARY
DataSet DType Table Chunk_Size
252720 /Grids/G1/precipTotRate/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 252720 | 2.215 | 0.958 | 0.000 | 1.966 | 7.487 | 176.814 | 0.000 | 122.311 | 0.000 | 1.304 | 71,708.000 |
SUMMARY
DataSet DType Table Chunk_Size
254340 /Grids/G1/precipTotRate/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 254340 | 2.215 | 1.129 | 0.000 | 2.648 | 4.428 | 34.107 | 0.000 | 83.595 | 0.000 | 0.935 | 68,784.000 |
SUMMARY
DataSet DType Table Chunk_Size
255960 /Grids/G1/precipTotWaterContent/count int32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 255960 | 2.215 | 448.131 | 0.000 | 1,326.512 | 5.267 | 40.665 | 0.000 | 28,568.000 | 0.000 | 133.000 | 71,660.000 |
SUMMARY
DataSet DType Table Chunk_Size
257580 /Grids/G1/precipTotWaterContent/hist int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 | 5.000 |
| mean | 13.289 | 12.451 | 0.000 | 52.148 | 11.020 | 208.264 | 0.000 | 2,301.600 | 0.000 | 1.000 | 332,331.600 |
| std | 6.062 | 11.237 | 0.000 | 42.869 | 3.464 | 128.644 | 0.000 | 1,730.232 | 0.000 | 1.732 | 195,962.175 |
| min | 2.445 | 0.261 | 0.000 | 1.994 | 7.174 | 79.899 | 0.000 | 108.000 | 0.000 | 0.000 | 32,025.000 |
| 25% | 16.000 | 1.962 | 0.000 | 10.808 | 8.621 | 117.807 | 0.000 | 897.000 | 0.000 | 0.000 | 252,178.000 |
| 50% | 16.000 | 12.938 | 0.000 | 67.805 | 10.065 | 150.893 | 0.000 | 3,050.000 | 0.000 | 0.000 | 414,383.000 |
| 75% | 16.000 | 23.513 | 0.000 | 88.349 | 14.069 | 339.163 | 0.000 | 3,193.000 | 0.000 | 1.000 | 427,965.000 |
| max | 16.000 | 23.580 | 0.000 | 91.784 | 15.173 | 353.560 | 0.000 | 4,260.000 | 0.000 | 4.000 | 535,107.000 |
SUMMARY
DataSet DType Table Chunk_Size
265680 /Grids/G1/precipTotWaterContent/mean float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 265680 | 2.215 | 0.196 | 0.000 | 0.344 | 3.503 | 29.819 | 0.000 | 9.445 | 0.000 | 0.341 | 71,660.000 |
SUMMARY
DataSet DType Table Chunk_Size
267300 /Grids/G1/precipTotWaterContent/stdev float32 0.000 2.215
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 267300 | 2.215 | 0.163 | 0.000 | 0.329 | 3.201 | 12.698 | 0.000 | 4.015 | 0.000 | 0.205 | 68,790.000 |
SUMMARY
DataSet DType Table Chunk_Size
268920 /Grids/G1/surfPrecipTotRateDiurnal/count int32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 268920 | 1.107 | 97.688 | 0.000 | 285.385 | 5.308 | 39.019 | 0.000 | 5,666.000 | 0.000 | 40.000 | 49,830.000 |
SUMMARY
DataSet DType Table Chunk_Size
270540 /Grids/G1/surfPrecipTotRateDiurnal/mean float32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 270540 | 1.107 | 0.591 | 0.000 | 1.355 | 16.012 | 879.167 | 0.000 | 128.023 | 0.000 | 0.796 | 49,830.000 |
SUMMARY
DataSet DType Table Chunk_Size
272160 /Grids/G1/surfPrecipTotRateDiurnal/stdev float32 0.000 1.107
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 272160 | 1.107 | 0.771 | 0.000 | 2.007 | 6.019 | 90.559 | 0.000 | 91.046 | 0.000 | 0.536 | 51,446.000 |
SUMMARY
DataSet DType Table Chunk_Size
273780 /Grids/G2/precipLiqRate/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.116 | 0.000 | 2.974 | 6.481 | 180.121 | 0.000 | 69.889 | 0.000 | 0.556 | 215,011.833 |
| std | 1.260 | 1.730 | 0.000 | 3.775 | 10.321 | 501.302 | 0.000 | 69.628 | 0.000 | 1.542 | 288,247.827 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.101 | 0.000 | 0.951 | 3.409 | 15.641 | 0.000 | 60.000 | 0.000 | 0.000 | 51,479.500 |
| 75% | 16.000 | 1.624 | 0.000 | 5.388 | 6.060 | 49.034 | 0.000 | 143.250 | 0.000 | 0.000 | 424,109.750 |
| max | 16.000 | 5.748 | 0.000 | 11.004 | 41.152 | 2,111.449 | 0.000 | 175.000 | 0.000 | 6.000 | 889,465.000 |
SUMMARY
DataSet DType Table Chunk_Size
302940 /Grids/G2/precipLiqRate/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.187 | 0.000 | 0.686 | 14.115 | 1,546.143 | 0.000 | 77.343 | 0.000 | 0.086 | 215,011.833 |
| std | 1.260 | 0.286 | 0.000 | 0.902 | 25.548 | 5,105.949 | 0.000 | 96.388 | 0.000 | 0.203 | 288,247.827 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.017 | 0.000 | 0.218 | 6.596 | 91.487 | 0.000 | 28.947 | 0.000 | 0.000 | 51,479.500 |
| 75% | 16.000 | 0.296 | 0.000 | 1.023 | 16.177 | 715.496 | 0.000 | 157.397 | 0.000 | 0.000 | 424,109.750 |
| max | 16.000 | 0.989 | 0.000 | 2.996 | 108.763 | 21,889.348 | 0.000 | 295.298 | 0.000 | 0.647 | 889,465.000 |
SUMMARY
DataSet DType Table Chunk_Size
332100 /Grids/G2/precipLiqRate/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.140 | 0.000 | 0.589 | 16.052 | 2,989.569 | 0.000 | 53.402 | 0.000 | 0.023 | 193,538.389 |
| std | 1.260 | 0.213 | 0.000 | 0.755 | 31.771 | 10,550.917 | 0.000 | 56.937 | 0.000 | 0.074 | 258,484.198 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.011 | 0.000 | 0.162 | 6.036 | 64.670 | 0.000 | 31.550 | 0.000 | 0.000 | 45,052.500 |
| 75% | 16.000 | 0.203 | 0.000 | 1.004 | 14.155 | 640.694 | 0.000 | 113.004 | 0.000 | 0.000 | 373,883.250 |
| max | 16.000 | 0.660 | 0.000 | 2.248 | 135.611 | 45,023.028 | 0.000 | 138.487 | 0.000 | 0.298 | 739,633.000 |
SUMMARY
DataSet DType Table Chunk_Size
361260 /Grids/G2/precipLiqWaterContent/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.116 | 0.000 | 2.974 | 6.481 | 180.082 | 0.000 | 69.889 | 0.000 | 0.556 | 215,012.167 |
| std | 1.260 | 1.730 | 0.000 | 3.774 | 10.320 | 501.151 | 0.000 | 69.628 | 0.000 | 1.542 | 288,248.481 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.101 | 0.000 | 0.951 | 3.409 | 15.641 | 0.000 | 60.000 | 0.000 | 0.000 | 51,477.500 |
| 75% | 16.000 | 1.624 | 0.000 | 5.388 | 6.060 | 49.035 | 0.000 | 143.250 | 0.000 | 0.000 | 424,113.250 |
| max | 16.000 | 5.748 | 0.000 | 11.004 | 41.147 | 2,110.792 | 0.000 | 175.000 | 0.000 | 6.000 | 889,463.000 |
SUMMARY
DataSet DType Table Chunk_Size
390420 /Grids/G2/precipLiqWaterContent/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.015 | 0.000 | 0.048 | 12.106 | 1,176.595 | 0.000 | 4.379 | 0.000 | 0.008 | 215,012.167 |
| std | 1.260 | 0.021 | 0.000 | 0.058 | 22.161 | 3,893.149 | 0.000 | 4.065 | 0.000 | 0.019 | 288,248.481 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.003 | 0.000 | 0.035 | 5.869 | 80.234 | 0.000 | 4.407 | 0.000 | 0.000 | 51,477.500 |
| 75% | 16.000 | 0.025 | 0.000 | 0.060 | 12.625 | 639.552 | 0.000 | 8.588 | 0.000 | 0.000 | 424,113.250 |
| max | 16.000 | 0.067 | 0.000 | 0.175 | 94.256 | 16,687.977 | 0.000 | 9.696 | 0.000 | 0.058 | 889,463.000 |
SUMMARY
DataSet DType Table Chunk_Size
419580 /Grids/G2/precipLiqWaterContent/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.010 | 0.000 | 0.038 | 11.486 | 835.252 | 0.000 | 2.259 | 0.000 | 0.002 | 193,539.500 |
| std | 1.260 | 0.014 | 0.000 | 0.044 | 20.344 | 2,660.245 | 0.000 | 1.947 | 0.000 | 0.006 | 258,485.563 |
| min | 10.656 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 |
| 50% | 16.000 | 0.002 | 0.000 | 0.025 | 5.575 | 55.408 | 0.000 | 2.821 | 0.000 | 0.000 | 45,052.500 |
| 75% | 16.000 | 0.015 | 0.000 | 0.052 | 9.562 | 141.844 | 0.000 | 4.013 | 0.000 | 0.000 | 373,887.750 |
| max | 16.000 | 0.040 | 0.000 | 0.118 | 85.130 | 11,360.029 | 0.000 | 4.575 | 0.000 | 0.025 | 739,627.000 |
SUMMARY
DataSet DType Table Chunk_Size
448740 /Grids/G2/precipTotDm/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.723 | 0.056 | 4.212 | 14.227 | 746.306 | 0.000 | 96.222 | 0.000 | 1.556 | 362,605.722 |
| std | 1.260 | 2.224 | 0.236 | 3.901 | 16.397 | 1,691.575 | 0.000 | 53.290 | 0.000 | 2.975 | 309,336.168 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.541 | 9.251 | 0.000 | 11.000 | 0.000 | 0.000 | 5,409.000 |
| 25% | 16.000 | 0.153 | 0.000 | 1.139 | 4.039 | 22.405 | 0.000 | 55.500 | 0.000 | 0.000 | 134,908.000 |
| 50% | 16.000 | 0.641 | 0.000 | 2.752 | 6.444 | 56.111 | 0.000 | 77.500 | 0.000 | 0.000 | 271,364.500 |
| 75% | 16.000 | 1.753 | 0.000 | 6.213 | 12.021 | 191.830 | 0.000 | 147.500 | 0.000 | 1.500 | 622,604.000 |
| max | 16.000 | 7.195 | 1.000 | 11.825 | 57.967 | 6,928.863 | 0.000 | 175.000 | 0.000 | 10.000 | 1,078,333.000 |
SUMMARY
DataSet DType Table Chunk_Size
477900 /Grids/G2/precipTotDm/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.162 | 0.040 | 0.274 | 6.114 | 101.237 | 0.000 | 3.361 | 0.000 | 0.249 | 366,540.778 |
| std | 1.260 | 0.182 | 0.170 | 0.191 | 8.065 | 249.992 | 0.000 | 0.958 | 0.000 | 0.417 | 315,105.787 |
| min | 10.656 | 0.001 | 0.000 | 0.020 | 0.157 | -1.389 | 0.000 | 1.158 | 0.000 | 0.000 | 5,409.000 |
| 25% | 16.000 | 0.025 | 0.000 | 0.126 | 1.196 | 0.233 | 0.000 | 3.235 | 0.000 | 0.000 | 134,908.000 |
| 50% | 16.000 | 0.089 | 0.000 | 0.242 | 3.015 | 8.887 | 0.000 | 3.856 | 0.000 | 0.000 | 271,364.500 |
| 75% | 16.000 | 0.301 | 0.000 | 0.445 | 5.547 | 32.356 | 0.000 | 3.959 | 0.000 | 0.575 | 622,604.000 |
| max | 16.000 | 0.564 | 0.720 | 0.578 | 31.921 | 1,046.805 | 0.000 | 3.999 | 0.000 | 1.032 | 1,078,333.000 |
SUMMARY
DataSet DType Table Chunk_Size
507060 /Grids/G2/precipTotDm/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.023 | 0.000 | 0.055 | 24.031 | 4,421.390 | 0.000 | 1.480 | 0.000 | 0.025 | 314,993.167 |
| std | 1.260 | 0.028 | 0.000 | 0.044 | 56.508 | 17,067.517 | 0.000 | 0.388 | 0.000 | 0.051 | 279,365.299 |
| min | 10.656 | 0.000 | 0.000 | 0.001 | 1.690 | 3.841 | 0.000 | 0.239 | 0.000 | 0.000 | 623.000 |
| 25% | 16.000 | 0.003 | 0.000 | 0.019 | 2.913 | 12.174 | 0.000 | 1.543 | 0.000 | 0.000 | 101,333.500 |
| 50% | 16.000 | 0.011 | 0.000 | 0.046 | 6.459 | 61.163 | 0.000 | 1.582 | 0.000 | 0.000 | 223,067.000 |
| 75% | 16.000 | 0.041 | 0.000 | 0.089 | 12.456 | 314.355 | 0.000 | 1.676 | 0.000 | 0.012 | 561,744.000 |
| max | 16.000 | 0.093 | 0.000 | 0.130 | 245.611 | 72,752.224 | 0.000 | 1.856 | 0.000 | 0.169 | 850,657.000 |
SUMMARY
DataSet DType Table Chunk_Size
536220 /Grids/G2/precipTotLogNw/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 2.106 | 0.167 | 4.762 | 12.146 | 418.776 | 0.000 | 105.944 | 0.000 | 2.056 | 402,087.056 |
| std | 1.260 | 2.671 | 0.514 | 4.371 | 12.597 | 701.827 | 0.000 | 59.545 | 0.000 | 3.811 | 332,801.195 |
| min | 10.656 | 0.004 | 0.000 | 0.078 | 2.413 | 8.330 | 0.000 | 11.000 | 0.000 | 0.000 | 15,001.000 |
| 25% | 16.000 | 0.157 | 0.000 | 1.162 | 3.538 | 16.850 | 0.000 | 55.750 | 0.000 | 0.000 | 140,314.000 |
| 50% | 16.000 | 1.268 | 0.000 | 3.507 | 5.710 | 44.225 | 0.000 | 85.000 | 0.000 | 0.000 | 334,713.500 |
| 75% | 16.000 | 2.011 | 0.000 | 7.067 | 11.787 | 184.479 | 0.000 | 155.000 | 0.000 | 2.000 | 645,989.500 |
| max | 16.000 | 8.564 | 2.000 | 13.245 | 37.539 | 1,884.219 | 0.000 | 193.000 | 0.000 | 13.000 | 1,116,373.000 |
SUMMARY
DataSet DType Table Chunk_Size
565380 /Grids/G2/precipTotLogNw/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.445 | 0.735 | 2.273 | 4.434 | 46.850 | 0.000 | 9.877 | 0.000 | 2.291 | 418,385.833 |
| std | 1.260 | 1.403 | 2.140 | 1.147 | 5.551 | 93.752 | 0.000 | 0.217 | 0.000 | 3.334 | 354,916.880 |
| min | 10.656 | 0.024 | 0.000 | 0.430 | -0.430 | -1.953 | 0.000 | 9.178 | 0.000 | 0.000 | 15,001.000 |
| 25% | 16.000 | 0.293 | 0.000 | 1.426 | 0.539 | -1.676 | 0.000 | 9.921 | 0.000 | 0.000 | 140,314.000 |
| 50% | 16.000 | 1.003 | 0.000 | 2.507 | 2.104 | 2.454 | 0.000 | 9.967 | 0.000 | 0.000 | 334,713.500 |
| 75% | 16.000 | 2.652 | 0.000 | 3.397 | 4.854 | 21.648 | 0.000 | 9.984 | 0.000 | 6.741 | 645,989.500 |
| max | 16.000 | 4.211 | 6.647 | 3.568 | 18.154 | 327.643 | 0.000 | 9.998 | 0.000 | 7.043 | 1,116,373.000 |
SUMMARY
DataSet DType Table Chunk_Size
594540 /Grids/G2/precipTotLogNw/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.033 | 0.003 | 0.075 | 15.739 | 1,878.335 | 0.000 | 1.474 | 0.000 | 0.038 | 353,617.222 |
| std | 1.260 | 0.035 | 0.013 | 0.043 | 31.126 | 7,120.602 | 0.000 | 0.206 | 0.000 | 0.068 | 311,016.740 |
| min | 10.656 | 0.000 | 0.000 | 0.002 | 1.284 | 1.706 | 0.000 | 0.865 | 0.000 | 0.000 | 2,881.000 |
| 25% | 16.000 | 0.005 | 0.000 | 0.040 | 3.015 | 10.717 | 0.000 | 1.371 | 0.000 | 0.000 | 104,553.000 |
| 50% | 16.000 | 0.023 | 0.000 | 0.080 | 4.557 | 26.191 | 0.000 | 1.509 | 0.000 | 0.000 | 259,464.000 |
| 75% | 16.000 | 0.043 | 0.000 | 0.106 | 9.583 | 107.163 | 0.000 | 1.636 | 0.000 | 0.041 | 643,178.500 |
| max | 16.000 | 0.113 | 0.054 | 0.139 | 134.109 | 30,370.566 | 0.000 | 1.709 | 0.000 | 0.199 | 902,887.000 |
SUMMARY
DataSet DType Table Chunk_Size
623700 /Grids/G2/precipTotRate/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.724 | 0.056 | 4.216 | 14.224 | 746.030 | 0.000 | 96.222 | 0.000 | 1.556 | 362,620.611 |
| std | 1.260 | 2.226 | 0.236 | 3.904 | 16.394 | 1,691.418 | 0.000 | 53.290 | 0.000 | 2.975 | 309,336.243 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.540 | 9.240 | 0.000 | 11.000 | 0.000 | 0.000 | 5,409.000 |
| 25% | 16.000 | 0.153 | 0.000 | 1.141 | 4.039 | 22.403 | 0.000 | 55.500 | 0.000 | 0.000 | 134,914.000 |
| 50% | 16.000 | 0.641 | 0.000 | 2.754 | 6.441 | 56.051 | 0.000 | 77.500 | 0.000 | 0.000 | 271,390.500 |
| 75% | 16.000 | 1.755 | 0.000 | 6.222 | 12.018 | 191.738 | 0.000 | 147.500 | 0.000 | 1.500 | 622,627.000 |
| max | 16.000 | 7.200 | 1.000 | 11.832 | 57.967 | 6,928.863 | 0.000 | 175.000 | 0.000 | 10.000 | 1,078,347.000 |
SUMMARY
DataSet DType Table Chunk_Size
652860 /Grids/G2/precipTotRate/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.313 | 0.019 | 1.063 | 43.524 | 11,967.816 | 0.000 | 160.361 | 0.000 | 0.222 | 366,536.500 |
| std | 1.260 | 0.330 | 0.080 | 0.841 | 55.596 | 24,314.865 | 0.000 | 72.186 | 0.000 | 0.373 | 315,075.384 |
| min | 10.656 | 0.001 | 0.000 | 0.080 | 4.918 | 52.856 | 0.000 | 41.717 | 0.000 | 0.000 | 5,409.000 |
| 25% | 16.000 | 0.075 | 0.000 | 0.450 | 8.727 | 201.248 | 0.000 | 96.891 | 0.000 | 0.000 | 134,914.000 |
| 50% | 16.000 | 0.207 | 0.000 | 0.899 | 19.486 | 1,210.433 | 0.000 | 193.685 | 0.000 | 0.000 | 271,390.500 |
| 75% | 16.000 | 0.470 | 0.000 | 1.443 | 58.571 | 9,298.809 | 0.000 | 209.951 | 0.000 | 0.511 | 622,627.000 |
| max | 16.000 | 1.116 | 0.341 | 3.098 | 199.003 | 85,613.265 | 0.000 | 242.941 | 0.000 | 1.005 | 1,078,347.000 |
SUMMARY
DataSet DType Table Chunk_Size
682020 /Grids/G2/precipTotRate/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.200 | 0.000 | 0.886 | 78.894 | 36,212.459 | 0.000 | 100.504 | 0.000 | 0.072 | 315,045.944 |
| std | 1.260 | 0.232 | 0.000 | 0.711 | 133.720 | 89,119.753 | 0.000 | 34.867 | 0.000 | 0.146 | 279,400.134 |
| min | 10.656 | 0.000 | 0.000 | 0.011 | 5.560 | 51.032 | 0.000 | 9.596 | 0.000 | 0.000 | 623.000 |
| 25% | 16.000 | 0.025 | 0.000 | 0.267 | 10.123 | 198.872 | 0.000 | 82.113 | 0.000 | 0.000 | 101,374.000 |
| 50% | 16.000 | 0.119 | 0.000 | 0.659 | 21.039 | 904.843 | 0.000 | 110.162 | 0.000 | 0.000 | 223,126.000 |
| 75% | 16.000 | 0.264 | 0.000 | 1.284 | 82.732 | 14,026.322 | 0.000 | 125.372 | 0.000 | 0.028 | 561,822.500 |
| max | 16.000 | 0.716 | 0.000 | 2.284 | 497.825 | 316,831.759 | 0.000 | 147.276 | 0.000 | 0.511 | 850,939.000 |
SUMMARY
DataSet DType Table Chunk_Size
711180 /Grids/G2/precipTotWaterContent/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 1.723 | 0.056 | 4.209 | 14.183 | 743.937 | 0.000 | 96.222 | 0.000 | 1.556 | 362,587.500 |
| std | 1.260 | 2.226 | 0.236 | 3.905 | 16.353 | 1,691.146 | 0.000 | 53.290 | 0.000 | 2.975 | 309,323.126 |
| min | 10.656 | 0.001 | 0.000 | 0.042 | 2.540 | 9.240 | 0.000 | 11.000 | 0.000 | 0.000 | 5,405.000 |
| 25% | 16.000 | 0.152 | 0.000 | 1.129 | 4.038 | 22.407 | 0.000 | 55.500 | 0.000 | 0.000 | 134,899.500 |
| 50% | 16.000 | 0.639 | 0.000 | 2.744 | 6.402 | 55.303 | 0.000 | 77.500 | 0.000 | 0.000 | 271,329.500 |
| 75% | 16.000 | 1.755 | 0.000 | 6.217 | 11.993 | 191.007 | 0.000 | 147.500 | 0.000 | 1.500 | 622,566.000 |
| max | 16.000 | 7.200 | 1.000 | 11.832 | 57.993 | 6,934.303 | 0.000 | 175.000 | 0.000 | 10.000 | 1,078,325.000 |
SUMMARY
DataSet DType Table Chunk_Size
740340 /Grids/G2/precipTotWaterContent/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.055 | 0.002 | 0.175 | 18.521 | 1,933.941 | 0.000 | 9.444 | 0.000 | 0.029 | 367,257.278 |
| std | 1.260 | 0.044 | 0.007 | 0.106 | 29.865 | 6,341.308 | 0.000 | 0.979 | 0.000 | 0.052 | 316,256.246 |
| min | 10.656 | 0.001 | 0.000 | 0.024 | 3.608 | 30.147 | 0.000 | 5.999 | 0.000 | 0.000 | 5,405.000 |
| 25% | 16.000 | 0.012 | 0.000 | 0.081 | 5.031 | 59.039 | 0.000 | 9.460 | 0.000 | 0.000 | 134,899.500 |
| 50% | 16.000 | 0.052 | 0.000 | 0.179 | 7.805 | 130.462 | 0.000 | 9.699 | 0.000 | 0.000 | 271,329.500 |
| 75% | 16.000 | 0.090 | 0.000 | 0.252 | 18.417 | 724.678 | 0.000 | 9.962 | 0.000 | 0.052 | 622,566.000 |
| max | 16.000 | 0.127 | 0.031 | 0.380 | 131.355 | 27,199.111 | 0.000 | 9.999 | 0.000 | 0.168 | 1,078,325.000 |
SUMMARY
DataSet DType Table Chunk_Size
769500 /Grids/G2/precipTotWaterContent/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 | 18.000 |
| mean | 15.703 | 0.027 | 0.000 | 0.110 | 43.110 | 16,377.813 | 0.000 | 4.423 | 0.000 | 0.011 | 315,032.278 |
| std | 1.260 | 0.024 | 0.000 | 0.074 | 106.849 | 64,718.794 | 0.000 | 0.605 | 0.000 | 0.022 | 279,403.781 |
| min | 10.656 | 0.000 | 0.000 | 0.004 | 4.683 | 40.449 | 0.000 | 2.904 | 0.000 | 0.000 | 625.000 |
| 25% | 16.000 | 0.005 | 0.000 | 0.046 | 6.613 | 70.571 | 0.000 | 4.289 | 0.000 | 0.000 | 101,360.500 |
| 50% | 16.000 | 0.020 | 0.000 | 0.102 | 9.280 | 145.461 | 0.000 | 4.713 | 0.000 | 0.000 | 223,072.000 |
| 75% | 16.000 | 0.042 | 0.000 | 0.145 | 23.470 | 1,071.809 | 0.000 | 4.826 | 0.000 | 0.003 | 561,814.000 |
| max | 16.000 | 0.076 | 0.000 | 0.235 | 463.993 | 275,478.034 | 0.000 | 4.910 | 0.000 | 0.079 | 850,931.000 |
SUMMARY
DataSet DType Table Chunk_Size
798660 /Grids/G2/surfPrecipTotRateDiurnal/count int32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.382 | 0.000 | 2.219 | 7.923 | 72.593 | 0.000 | 43.778 | 0.000 | 0.000 | 168,916.889 |
| std | 0.891 | 0.073 | 0.000 | 0.254 | 0.697 | 13.507 | 0.000 | 7.513 | 0.000 | 0.000 | 37,134.672 |
| min | 13.328 | 0.259 | 0.000 | 1.749 | 7.097 | 57.656 | 0.000 | 34.000 | 0.000 | 0.000 | 118,631.000 |
| 25% | 16.000 | 0.350 | 0.000 | 2.145 | 7.288 | 60.960 | 0.000 | 40.000 | 0.000 | 0.000 | 129,789.000 |
| 50% | 16.000 | 0.376 | 0.000 | 2.235 | 8.107 | 75.221 | 0.000 | 42.000 | 0.000 | 0.000 | 177,991.000 |
| 75% | 16.000 | 0.450 | 0.000 | 2.426 | 8.293 | 78.616 | 0.000 | 49.000 | 0.000 | 0.000 | 197,283.000 |
| max | 16.000 | 0.464 | 0.000 | 2.490 | 9.268 | 99.990 | 0.000 | 57.000 | 0.000 | 0.000 | 216,083.000 |
SUMMARY
DataSet DType Table Chunk_Size
813240 /Grids/G2/surfPrecipTotRateDiurnal/mean float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.075 | 0.000 | 0.585 | 45.298 | 9,108.544 | 0.000 | 174.894 | 0.000 | 0.000 | 168,916.889 |
| std | 0.891 | 0.018 | 0.000 | 0.110 | 26.393 | 12,017.392 | 0.000 | 27.034 | 0.000 | 0.000 | 37,134.672 |
| min | 13.328 | 0.049 | 0.000 | 0.389 | 21.534 | 1,038.742 | 0.000 | 126.668 | 0.000 | 0.000 | 118,631.000 |
| 25% | 16.000 | 0.066 | 0.000 | 0.548 | 33.069 | 3,453.626 | 0.000 | 164.262 | 0.000 | 0.000 | 129,789.000 |
| 50% | 16.000 | 0.073 | 0.000 | 0.579 | 39.312 | 4,815.003 | 0.000 | 179.099 | 0.000 | 0.000 | 177,991.000 |
| 75% | 16.000 | 0.082 | 0.000 | 0.613 | 41.770 | 6,277.041 | 0.000 | 185.010 | 0.000 | 0.000 | 197,283.000 |
| max | 16.000 | 0.104 | 0.000 | 0.756 | 107.043 | 39,446.954 | 0.000 | 210.769 | 0.000 | 0.000 | 216,083.000 |
SUMMARY
DataSet DType Table Chunk_Size
827820 /Grids/G2/surfPrecipTotRateDiurnal/stdev float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 | 9.000 |
| mean | 15.703 | 0.049 | 0.000 | 0.510 | 26.690 | 1,513.963 | 0.000 | 76.010 | 0.000 | 0.000 | 137,740.667 |
| std | 0.891 | 0.018 | 0.000 | 0.148 | 9.859 | 1,580.024 | 0.000 | 24.781 | 0.000 | 0.000 | 29,537.064 |
| min | 13.328 | 0.021 | 0.000 | 0.262 | 18.284 | 479.720 | 0.000 | 52.397 | 0.000 | 0.000 | 99,243.000 |
| 25% | 16.000 | 0.040 | 0.000 | 0.482 | 22.515 | 777.610 | 0.000 | 61.558 | 0.000 | 0.000 | 106,799.000 |
| 50% | 16.000 | 0.047 | 0.000 | 0.516 | 23.762 | 962.616 | 0.000 | 66.855 | 0.000 | 0.000 | 142,579.000 |
| 75% | 16.000 | 0.051 | 0.000 | 0.523 | 28.078 | 1,278.061 | 0.000 | 87.402 | 0.000 | 0.000 | 158,997.000 |
| max | 16.000 | 0.077 | 0.000 | 0.737 | 51.318 | 5,616.062 | 0.000 | 133.155 | 0.000 | 0.000 | 175,667.000 |
SUMMARY
DataSet DType Table Chunk_Size
842400 /Tair_2m float32 0.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 | 91.000 |
| mean | 15.907 | -492.861 | -637.851 | 507.856 | 0.008 | -1.997 | -999.000 | 30.934 | -999.000 | 19.359 | 17,212.011 |
| std | 0.885 | 11.484 | 481.442 | 0.261 | 0.046 | 0.002 | 0.000 | 0.748 | 0.000 | 0.467 | 1,706.482 |
| min | 7.559 | -509.377 | -999.000 | 506.906 | -0.112 | -1.999 | -999.000 | 29.715 | -999.000 | 18.537 | 7,763.000 |
| 25% | 16.000 | -500.751 | -999.000 | 507.769 | -0.025 | -1.999 | -999.000 | 30.217 | -999.000 | 18.981 | 16,068.000 |
| 50% | 16.000 | -497.201 | -999.000 | 507.882 | 0.025 | -1.998 | -999.000 | 30.961 | -999.000 | 19.338 | 17,327.000 |
| 75% | 16.000 | -484.812 | -3.709 | 507.998 | 0.039 | -1.996 | -999.000 | 31.538 | -999.000 | 19.614 | 18,260.500 |
| max | 16.000 | -462.618 | -0.219 | 508.295 | 0.073 | -1.987 | -999.000 | 32.856 | -999.000 | 20.471 | 19,826.000 |
SUMMARY
DataSet DType Table Chunk_Size
989820 /msft/table.index int64 1.000 5.087
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 989820 | 5.087 | 333,376.500 | 333,376.500 | 192,475.301 | -0.000 | -1.200 | 0.000 | 666,753.000 | 166,688.250 | 500,064.750 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
991440 /msft/table.values_block_0 float64 1.000 16.000
1001160 /msft/table.values_block_0 float64 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 | 6.000 |
| mean | 13.565 | 2,081,623.879 | 33.159 | 8,049,441.324 | 4.057 | 15.290 | 0.833 | 39,586,285.667 | 33.055 | 117.667 | 463,691.500 |
| std | 5.964 | 1,410,956.193 | 0.139 | 4,886,982.358 | 0.580 | 5.429 | 0.408 | 19,712,436.404 | 0.113 | 72.345 | 202,272.718 |
| min | 1.391 | 351,120.326 | 33.016 | 1,567,594.696 | 3.320 | 9.041 | 0.000 | 11,157,436.000 | 32.950 | 63.000 | 60,162.000 |
| 25% | 16.000 | 1,226,555.500 | 33.047 | 4,935,090.450 | 3.706 | 11.862 | 1.000 | 27,689,908.500 | 32.975 | 86.250 | 486,808.000 |
| 50% | 16.000 | 1,868,692.201 | 33.130 | 7,848,154.048 | 3.990 | 14.560 | 1.000 | 40,646,306.500 | 33.009 | 93.500 | 537,111.000 |
| 75% | 16.000 | 2,851,883.458 | 33.280 | 11,204,784.394 | 4.371 | 17.424 | 1.000 | 53,356,485.500 | 33.146 | 106.000 | 570,120.500 |
| max | 16.000 | 4,209,114.739 | 33.330 | 14,745,014.725 | 4.931 | 24.234 | 1.000 | 64,103,344.000 | 33.210 | 262.000 | 596,601.000 |
SUMMARY
DataSet DType Table Chunk_Size
1010880 /msft/table.values_block_1 int64 1.000 5.087
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1010880 | 5.087 | -4.000 | -4.000 | 0.000 | 0.000 | -3.000 | -4.000 | -4.000 | -4.000 | -4.000 | 1.000 |
SUMMARY
DataSet DType Table Chunk_Size
1012500 /msft/table.values_block_2 |S49 1.000 16.000
1025460 /msft/table.values_block_2 |S49 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 | 24.000 |
| mean | 15.579 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| std | 2.064 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| min | 5.889 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 25% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 50% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 75% | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| max | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
SUMMARY
DataSet DType Table Chunk_Size
1038420 /s501/events.event_id uint32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1038420 | 6.500 | 851,929.000 | 851,929.000 | 491,861.726 | 0.000 | -1.200 | 0.000 | 1,703,858.000 | 425,964.500 | 1,277,893.500 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1040040 /s501/events.timestamp int32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1040040 | 6.500 | 1,358,459,542.078 | 1,358,622,091.000 | 795,493.396 | -0.278 | -1.142 | 1,356,998,400.000 | 1,359,676,795.000 | 1,357,915,976.000 | 1,359,154,499.500 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1041660 /s501/events.nanoseconds uint32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1041660 | 6.500 | 500,344,402.532 | 500,861,254.000 | 288,787,763.801 | -0.002 | -1.201 | 411.000 | 999,999,183.000 | 250,240,652.000 | 750,517,316.500 | 840,201.000 |
SUMMARY
DataSet DType Table Chunk_Size
1043280 /s501/events.ext_timestamp uint64 1.000 12.999
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1043280 | 12.999 | 1,358,459,542,578,043,904.000 | 1,358,622,091,149,467,648.000 | 795,493,396,001,273.125 | -0.278 | -1.142 | 1,356,998,400,075,223,040.000 | 1,359,676,795,284,214,272.000 | 1,357,915,976,235,253,760.000 | 1,359,154,499,978,547,968.000 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1044900 /s501/events.pulseheights int16 1.000 12.999
1046520 /s501/events.pulseheights int16 2.000 12.999
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1044900 | 12.999 | 225.198 | 165.000 | 307.419 | 3.050 | 16.658 | 0.000 | 3,902.000 | 2.000 | 340.000 | 3,944,973.000 |
SUMMARY
DataSet DType Table Chunk_Size
1048140 /s501/events.integrals int32 1.000 16.000
1051380 /s501/events.integrals int32 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 |
| mean | 12.999 | 2,938.001 | 1,897.000 | 5,262.488 | 12.203 | 719.805 | 0.000 | 902,098.000 | 0.000 | 4,116.500 | 1,971,107.000 |
| std | 4.243 | 20.301 | 16.971 | 51.185 | 0.005 | 132.078 | 0.000 | 188,424.158 | 0.000 | 23.335 | 645,116.144 |
| min | 9.999 | 2,923.646 | 1,885.000 | 5,226.295 | 12.199 | 626.412 | 0.000 | 768,862.000 | 0.000 | 4,100.000 | 1,514,941.000 |
| 25% | 11.499 | 2,930.824 | 1,891.000 | 5,244.392 | 12.201 | 673.109 | 0.000 | 835,480.000 | 0.000 | 4,108.250 | 1,743,024.000 |
| 50% | 12.999 | 2,938.001 | 1,897.000 | 5,262.488 | 12.203 | 719.805 | 0.000 | 902,098.000 | 0.000 | 4,116.500 | 1,971,107.000 |
| 75% | 14.500 | 2,945.179 | 1,903.000 | 5,280.585 | 12.204 | 766.502 | 0.000 | 968,716.000 | 0.000 | 4,124.750 | 2,199,190.000 |
| max | 16.000 | 2,952.356 | 1,909.000 | 5,298.681 | 12.206 | 813.198 | 0.000 | 1,035,334.000 | 0.000 | 4,133.000 | 2,427,273.000 |
SUMMARY
DataSet DType Table Chunk_Size
1054620 /s501/events.n1 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1054620 | 6.500 | 0.861 | 0.497 | 1.674 | 14.735 | 1,198.104 | 0.000 | 332.660 | 0.000 | 1.199 | 852,108.000 |
SUMMARY
DataSet DType Table Chunk_Size
1056240 /s501/events.n2 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1056240 | 6.500 | 0.940 | 0.741 | 1.629 | 11.418 | 510.538 | 0.000 | 221.920 | 0.000 | 1.263 | 852,101.000 |
SUMMARY
DataSet DType Table Chunk_Size
1057860 /s501/events.n3 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1057860 | 6.500 | 0.824 | 0.457 | 1.448 | 11.162 | 576.769 | 0.000 | 189.520 | 0.000 | 1.181 | 851,761.000 |
SUMMARY
DataSet DType Table Chunk_Size
1059480 /s501/events.n4 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1059480 | 6.500 | 0.828 | 0.483 | 1.437 | 10.718 | 553.121 | 0.000 | 206.250 | 0.000 | 1.186 | 850,639.000 |
SUMMARY
DataSet DType Table Chunk_Size
1061100 /s501/events.t1 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1061100 | 6.500 | -389.194 | 12.500 | 618.172 | 0.662 | 1.053 | -999.000 | 5,017.500 | -999.000 | 15.000 | 793,348.000 |
SUMMARY
DataSet DType Table Chunk_Size
1062720 /s501/events.t2 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1062720 | 6.500 | -324.376 | 12.500 | 603.070 | 0.444 | 0.892 | -999.000 | 5,010.000 | -999.000 | 15.000 | 820,662.000 |
SUMMARY
DataSet DType Table Chunk_Size
1064340 /s501/events.t3 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1064340 | 6.500 | -370.608 | 12.500 | 623.015 | 0.626 | 0.966 | -999.000 | 5,022.500 | -999.000 | 22.500 | 836,835.000 |
SUMMARY
DataSet DType Table Chunk_Size
1065960 /s501/events.t4 float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1065960 | 6.500 | -366.309 | 12.500 | 621.876 | 0.617 | 1.000 | -999.000 | 5,417.500 | -999.000 | 22.500 | 832,499.000 |
SUMMARY
DataSet DType Table Chunk_Size
1067580 /s501/events.t_trigger float32 1.000 6.500
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1067580 | 6.500 | 266.529 | 32.500 | 424.564 | 1.584 | 1.236 | -999.000 | 4,085.000 | 22.500 | 352.500 | 849,577.000 |
SUMMARY
DataSet DType Table Chunk_Size
1069200 /s503/events.event_id uint32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1069200 | 3.853 | 505,008.000 | 505,008.000 | 291,566.793 | -0.000 | -1.200 | 0.000 | 1,010,016.000 | 252,504.000 | 757,512.000 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1070820 /s503/events.timestamp int32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1070820 | 3.853 | 1,358,393,491.580 | 1,358,421,675.000 | 757,500.324 | -0.109 | -1.104 | 1,356,998,404.000 | 1,359,676,799.000 | 1,357,774,690.000 | 1,359,037,251.000 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1072440 /s503/events.nanoseconds uint32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1072440 | 3.853 | 500,140,199.769 | 500,234,225.000 | 288,843,119.810 | -0.001 | -1.201 | 2,274.000 | 999,999,975.000 | 250,049,507.000 | 750,603,755.000 | 502,080.000 |
SUMMARY
DataSet DType Table Chunk_Size
1074060 /s503/events.ext_timestamp uint64 1.000 7.706
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1074060 | 7.706 | 1,358,393,492,080,139,008.000 | 1,358,421,675,792,917,248.000 | 757,500,324,792,366.125 | -0.109 | -1.104 | 1,356,998,404,761,455,616.000 | 1,359,676,799,029,137,152.000 | 1,357,774,690,227,434,240.000 | 1,359,037,251,568,953,600.000 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1075680 /s503/events.pulseheights int16 1.000 7.706
1077300 /s503/events.pulseheights int16 2.000 7.706
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1075680 | 7.706 | 196.302 | 148.000 | 289.952 | 3.614 | 22.678 | -999.000 | 3,907.000 | 2.000 | 284.000 | 2,554,584.000 |
SUMMARY
DataSet DType Table Chunk_Size
1078920 /s503/events.integrals int32 1.000 15.412
1080540 /s503/events.integrals int32 2.000 15.412
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1078920 | 15.412 | 2,420.894 | 1,519.000 | 4,793.930 | 11.797 | 588.802 | -999.000 | 860,218.000 | 0.000 | 3,205.000 | 2,559,878.000 |
SUMMARY
DataSet DType Table Chunk_Size
1082160 /s503/events.n1 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1082160 | 3.853 | 1.270 | 0.955 | 2.242 | -80.326 | 39,532.396 | -999.000 | 217.500 | 0.000 | 1.525 | 504,547.000 |
SUMMARY
DataSet DType Table Chunk_Size
1083780 /s503/events.n2 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1083780 | 3.853 | 1.383 | 1.016 | 2.698 | -37.286 | 19,597.590 | -999.000 | 440.720 | 0.000 | 1.576 | 504,333.000 |
SUMMARY
DataSet DType Table Chunk_Size
1085400 /s503/events.n3 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1085400 | 3.853 | 1.239 | 0.951 | 1.922 | 8.119 | 164.641 | 0.000 | 118.910 | 0.000 | 1.470 | 504,474.000 |
SUMMARY
DataSet DType Table Chunk_Size
1087020 /s503/events.n4 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1087020 | 3.853 | -926.881 | -999.000 | 258.545 | 3.306 | 8.930 | -999.000 | 1.016 | -999.000 | -999.000 | 5.000 |
SUMMARY
DataSet DType Table Chunk_Size
1088640 /s503/events.t1 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1088640 | 3.853 | -192.065 | 15.000 | 566.857 | 0.167 | 1.525 | -999.000 | 5,175.000 | -999.000 | 22.500 | 464,317.000 |
SUMMARY
DataSet DType Table Chunk_Size
1090260 /s503/events.t2 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1090260 | 3.853 | -181.615 | 12.500 | 554.753 | 0.195 | 2.279 | -999.000 | 5,742.500 | -999.000 | 17.500 | 501,010.000 |
SUMMARY
DataSet DType Table Chunk_Size
1091880 /s503/events.t3 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1091880 | 3.853 | -198.000 | 15.000 | 567.869 | 0.133 | 1.210 | -999.000 | 5,022.500 | -999.000 | 25.000 | 493,459.000 |
SUMMARY
DataSet DType Table Chunk_Size
1093500 /s503/events.t4 float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1093500 | 3.853 | -996.946 | -999.000 | 56.500 | 30.293 | 1,011.642 | -999.000 | 2,152.500 | -999.000 | -999.000 | 2,895.000 |
SUMMARY
DataSet DType Table Chunk_Size
1095120 /s503/events.t_trigger float32 1.000 3.853
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1095120 | 3.853 | 223.840 | 30.000 | 397.530 | 1.851 | 2.396 | -999.000 | 2,492.500 | 22.500 | 127.500 | 501,305.000 |
SUMMARY
DataSet DType Table Chunk_Size
1096740 /s506/events.event_id uint32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1096740 | 7.224 | 946,899.500 | 946,899.500 | 546,692.970 | -0.000 | -1.200 | 0.000 | 1,893,799.000 | 473,449.750 | 1,420,349.250 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1098360 /s506/events.timestamp int32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1098360 | 7.224 | 1,358,368,492.787 | 1,358,391,572.000 | 770,277.466 | -0.071 | -1.165 | 1,356,998,401.000 | 1,359,676,799.000 | 1,357,707,136.000 | 1,359,026,998.250 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1099980 /s506/events.nanoseconds uint32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1099980 | 7.224 | 500,067,570.365 | 499,978,651.500 | 288,547,420.591 | 0.000 | -1.199 | 293.000 | 999,999,359.000 | 250,426,981.750 | 749,994,019.500 | 936,004.000 |
SUMMARY
DataSet DType Table Chunk_Size
1101600 /s506/events.ext_timestamp uint64 1.000 14.449
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1101600 | 14.449 | 1,358,368,493,286,801,920.000 | 1,358,391,572,633,704,192.000 | 770,277,465,959,816.000 | -0.071 | -1.165 | 1,356,998,401,279,154,432.000 | 1,359,676,799,663,922,944.000 | 1,357,707,136,778,230,272.000 | 1,359,026,998,468,235,520.000 | 2.000 |
SUMMARY
DataSet DType Table Chunk_Size
1103220 /s506/events.pulseheights int16 1.000 14.449
1104840 /s506/events.pulseheights int16 2.000 14.449
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1103220 | 14.449 | 216.367 | 169.000 | 297.434 | 3.165 | 17.493 | -999.000 | 3,911.000 | 2.000 | 320.000 | 4,367,308.000 |
SUMMARY
DataSet DType Table Chunk_Size
1106460 /s506/events.integrals int32 1.000 16.000
1109700 /s506/events.integrals int32 2.000 16.000
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 |
| mean | 14.449 | 2,856.271 | 2,063.000 | 5,208.375 | 22.535 | 4,115.081 | -499.500 | 1,451,594.000 | 0.000 | 3,946.000 | 2,182,572.500 |
| std | 2.194 | 36.075 | 31.113 | 214.802 | 11.803 | 4,142.660 | 706.400 | 741,937.447 | 0.000 | 24.042 | 336,617.476 |
| min | 12.897 | 2,830.762 | 2,041.000 | 5,056.487 | 14.189 | 1,185.778 | -999.000 | 926,965.000 | 0.000 | 3,929.000 | 1,944,548.000 |
| 25% | 13.673 | 2,843.517 | 2,052.000 | 5,132.431 | 18.362 | 2,650.429 | -749.250 | 1,189,279.500 | 0.000 | 3,937.500 | 2,063,560.250 |
| 50% | 14.449 | 2,856.271 | 2,063.000 | 5,208.375 | 22.535 | 4,115.081 | -499.500 | 1,451,594.000 | 0.000 | 3,946.000 | 2,182,572.500 |
| 75% | 15.224 | 2,869.026 | 2,074.000 | 5,284.319 | 26.708 | 5,579.732 | -249.750 | 1,713,908.500 | 0.000 | 3,954.500 | 2,301,584.750 |
| max | 16.000 | 2,881.780 | 2,085.000 | 5,360.263 | 30.881 | 7,044.384 | 0.000 | 1,976,223.000 | 0.000 | 3,963.000 | 2,420,597.000 |
SUMMARY
DataSet DType Table Chunk_Size
1112940 /s506/events.n1 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1112940 | 7.224 | 0.906 | 0.593 | 1.896 | -51.101 | 46,506.397 | -999.000 | 593.210 | 0.000 | 1.251 | 948,502.000 |
SUMMARY
DataSet DType Table Chunk_Size
1114560 /s506/events.n2 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1114560 | 7.224 | 0.965 | 0.788 | 1.637 | 18.847 | 2,319.232 | 0.000 | 371.000 | 0.000 | 1.296 | 948,104.000 |
SUMMARY
DataSet DType Table Chunk_Size
1116180 /s506/events.n3 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1116180 | 7.224 | 0.860 | 0.553 | 1.942 | -134.250 | 74,975.796 | -999.000 | 310.790 | 0.000 | 1.207 | 946,423.000 |
SUMMARY
DataSet DType Table Chunk_Size
1117800 /s506/events.n4 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1117800 | 7.224 | 0.890 | 0.603 | 1.544 | 13.214 | 1,255.583 | -1.000 | 321.740 | 0.000 | 1.259 | 947,489.000 |
SUMMARY
DataSet DType Table Chunk_Size
1119420 /s506/events.t1 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1119420 | 7.224 | -370.239 | 12.500 | 611.587 | 0.584 | 0.980 | -999.000 | 5,022.500 | -999.000 | 15.000 | 845,656.000 |
SUMMARY
DataSet DType Table Chunk_Size
1121040 /s506/events.t2 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1121040 | 7.224 | -307.761 | 12.500 | 598.034 | 0.377 | 0.818 | -999.000 | 5,017.500 | -999.000 | 20.000 | 938,072.000 |
SUMMARY
DataSet DType Table Chunk_Size
1122660 /s506/events.t3 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1122660 | 7.224 | -386.432 | 12.500 | 616.676 | 0.677 | 1.328 | -999.000 | 5,385.000 | -999.000 | 17.500 | 911,415.000 |
SUMMARY
DataSet DType Table Chunk_Size
1124280 /s506/events.t4 float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1124280 | 7.224 | -346.851 | 12.500 | 621.689 | 0.554 | 0.866 | -999.000 | 5,122.500 | -999.000 | 22.500 | 934,425.000 |
SUMMARY
DataSet DType Table Chunk_Size
1125900 /s506/events.t_trigger float32 1.000 7.224
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1125900 | 7.224 | 250.095 | 32.500 | 414.191 | 1.686 | 1.643 | -999.000 | 4,997.500 | 22.500 | 267.500 | 940,519.000 |
No entraremos en detalles sobre cada conjunto de datos, simplemente nos conviene tener estas tablas como referencia rápida en caso de detectar anomalías en algún conjunto en concreto.
Para evitar que los diagramas de caja esten plagados de datos atípicos, procedemos a filtrar con el codec blosclz, filtro shuffle, nivel de compresión 5 y tamaño de bloque automático para buscar con detenimiento datos atípicos.
df_outliers = my_df[(my_df.Block_Size == 0) & (my_df.CL == 5) &
(my_df.Codec == 'blosclz') &
(my_df.Filter == 'noshuffle')]
cst.paint_dtype_boxplots(df_outliers)
Mostramos a continuación los datos atípicos
for i in range(2):
dfaux = df_outliers[df_outliers.DType.str.contains(cst.TYPES[i])]
if dfaux.size > 0:
cr_lim = cst.outlier_lim(dfaux['CRate'])
cs_lim = cst.outlier_lim(dfaux['CSpeed'])
ds_lim = cst.outlier_lim(dfaux['DSpeed'])
result = dfaux[(dfaux.CRate < cr_lim[0]) |
(dfaux.CRate > cr_lim[1]) |
(dfaux.CSpeed < cs_lim[0]) |
(dfaux.CSpeed > cs_lim[1]) |
(dfaux.DSpeed < ds_lim[0]) |
(dfaux.DSpeed > ds_lim[1])][cst.ALL_FEATURES]
if result.size > 0:
print('%d %s OUTLIERS' % (result.shape[0],
cst.TYPES[i].upper()))
display(result.head())
81 FLOAT OUTLIERS
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84244 | 1.475 | 7.835 | 6.866 | 6.404 | 0.414 | 0.209 | -12.035 | 27.183 | 4.119 | 11.317 | 13,756.000 | 1.000 | 1.739 | 29.472 |
| 200884 | 2.215 | 0.456 | 0.000 | 1.532 | 8.146 | 210.120 | 0.000 | 122.311 | 0.000 | 0.000 | 28,860.000 | 2.935 | 3.793 | 38.118 |
| 202504 | 2.215 | 0.650 | 0.000 | 2.098 | 4.997 | 32.385 | 0.000 | 43.932 | 0.000 | 0.000 | 29,002.000 | 3.036 | 4.552 | 33.353 |
| 213844 | 2.215 | 0.036 | 0.000 | 0.104 | 5.367 | 54.662 | 0.000 | 4.711 | 0.000 | 0.000 | 28,858.000 | 2.935 | 4.428 | 39.103 |
| 215464 | 2.215 | 0.044 | 0.000 | 0.127 | 4.180 | 23.454 | 0.000 | 3.249 | 0.000 | 0.000 | 29,004.000 | 3.036 | 4.566 | 36.434 |
38 INT OUTLIERS
| Chunk_Size | Mean | Median | Sd | Skew | Kurt | Min | Max | Q1 | Q3 | N_Streaks | CRate | CSpeed | DSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 199264 | 2.445 | 0.000 | 0.000 | 0.027 | 99.485 | 18,684.148 | 0.000 | 8.000 | 0.000 | 0.000 | 505.000 | 156.816 | 20.032 | 74.193 |
| 212224 | 2.445 | 0.002 | 0.000 | 0.055 | 50.616 | 3,993.813 | 0.000 | 8.000 | 0.000 | 0.000 | 1,523.000 | 134.316 | 19.042 | 22.407 |
| 231664 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 170.639 | 19.035 | 29.024 |
| 251104 | 2.445 | 0.001 | 0.000 | 0.045 | 47.237 | 3,683.632 | 0.000 | 7.000 | 0.000 | 0.000 | 1,533.000 | 135.187 | 19.078 | 64.619 |
| 278644 | 16.000 | 0.000 | 0.000 | 0.000 | 0.000 | -3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | 170.639 | 19.401 | 30.187 |
No mostramos los datos atípicos de tipo string dado que no extraemos ninguna característica de chunk que podamos comentar.
En cuanto a los datos atípicos observamos que la mayoría son series de números idénticos o muy parecidos, siempre con un rango intercuartílico de 0.
Aquí pretendemos observar la correlación entre el tamaño de bloque y las medidas de compresión, para ello filtramos los datos por tipo, codec, filtro, nivel de compresión y tamaño de bloque; y calculamos la media de su ratio de compresión y velocidades de compresión/decompresión.
Las gráficas presentan los ratios de compresión (en azul) y las velocidades de compresión y de descompresión (en rojo y verde) medios para cada tamaño de bloque. Primero mostramos estos datos para los datos de tipo float y de tipo int.
cst.paint_all_block_cor(my_df, 'shuffle', c_level=5)
Aquí se muestran los mismos gráficos pero para los datos del tipo cadenas de texto
cst.paint_all_block_cor(my_df, 'noshuffle')
Como podemos observar, al aumentar el tamaño de bloque suele aumentar el ratio de compresión pero parece converger hasta un límite entre los tamaños de 512 KB y 2 MB. Además cuando el tamaño de bloque es menor en general las velocidades son más rápidas.
Por otro lado destaca el comportamiento de Snappy pues no parece comprimir muy bien con respecto al resto. Por otro lado Zlib parece ser inferior en todo a Zstd.
Aquí se presentan las mismas gráficas pero alterando el nivel de compresión para ver como afecta al tamaño de bloque.
cst.paint_cl_comparison(my_df, 'shuffle', 'blosclz')
cst.paint_cl_comparison(my_df, 'shuffle', 'lz4')
Los resultados son los esperados el comportamiento es en general el mismo, simplemente suben los ratio de compresión y bajan las velocidades a medida que aumenta el nivel de compresión. Por otra parte destaca el comportamiento del tamaño de bloque automático observamos que está programado para que aumente conjuntamente con el nivel de compresión.
Al igual que en el anterior caso hacemos los mismos gráficos pero observando el nivel de compresión.
# BLOCK SIZE --> CL
cst.paint_all_block_cor(my_df, 'shuffle', block_size=256, cl_mode=True)
cst.paint_all_block_cor(my_df, 'noshuffle', block_size=256, cl_mode=True)
Destaca el comportamiento de Snappy de nuevo vuelve a ser el más raro de todos, el nivel de compresión no cambia nada. Por otro lado Zlib tiene un cambio brusco a partir del nivel de compresión 3, esto se debe a que a partir de ese nivel activa métodos más potentes a la hora de comprimir. Finalmente Zstd parece hacer lo mismo que Zlib, pero parece que en los últimos niveles de compresión no funciona bien, pues pierde ratio de compresión.
En el caso de que los datos esten en forma de tabla, si la tabla contiene más de una columna se realizan dos pruebas de compresión, una guardando los datos como tabla normal, fila por fila y otra guardándolos columnarmente.
df_col = my_df[my_df.Table == 2]
if df_col.size > 0:
sets = df_col.drop_duplicates(subset=['DataSet'])
for dataset in sets['DataSet']:
dfaux = my_df[my_df.DataSet == dataset]
normal_table = dfaux[dfaux.Table == 1][cst.TEST_FEATURES]
normal_table.columns = ['N_CRate', 'N_CSpeed', 'N_DSpeed']
col_table = dfaux[dfaux.Table == 2][cst.TEST_FEATURES]
col_table.columns = ['COL_CRate', 'COL_CSpeed', 'COL_DSpeed']
result = pd.concat([normal_table, col_table])
result = result[['N_CRate', 'COL_CRate', 'N_CSpeed',
'COL_CSpeed','N_DSpeed', 'COL_DSpeed']]
print(sets[sets.DataSet == dataset][cst.DESC_SET])
display(result.describe())
DataSet DType Table Chunk_Size 1001160 /msft/table.values_block_0 float64 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 | 9,720.000 |
| mean | 10.462 | 17.942 | 2.063 | 2.281 | 5.760 | 4.986 |
| std | 7.310 | 33.079 | 2.237 | 2.462 | 4.037 | 3.204 |
| min | 1.000 | 1.000 | 0.002 | 0.002 | 0.361 | 0.345 |
| 25% | 4.342 | 5.133 | 0.413 | 0.401 | 2.599 | 2.595 |
| 50% | 8.844 | 8.050 | 1.249 | 1.557 | 4.425 | 4.276 |
| 75% | 14.532 | 15.924 | 3.038 | 3.397 | 8.164 | 6.744 |
| max | 39.004 | 297.005 | 10.584 | 15.156 | 28.628 | 28.773 |
DataSet DType Table Chunk_Size 1025460 /msft/table.values_block_2 |S49 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 | 12,960.000 |
| mean | 52.373 | 147.258 | 6.509 | 6.845 | 11.466 | 10.729 |
| std | 45.062 | 737.652 | 5.962 | 6.167 | 3.926 | 3.685 |
| min | 1.000 | 1.000 | 0.005 | 0.007 | 1.862 | 1.881 |
| 25% | 7.624 | 21.154 | 1.176 | 1.012 | 10.437 | 9.548 |
| 50% | 46.981 | 47.146 | 4.621 | 5.745 | 12.911 | 11.920 |
| 75% | 72.699 | 86.410 | 11.163 | 11.211 | 13.708 | 13.126 |
| max | 234.129 | 10,131.164 | 20.215 | 22.567 | 34.169 | 30.888 |
DataSet DType Table Chunk_Size 1046520 /s501/events.pulseheights int16 2.000 12.999
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 |
| mean | 1.412 | 1.408 | 1.788 | 1.545 | 5.182 | 4.917 |
| std | 0.314 | 0.307 | 2.634 | 2.243 | 4.310 | 4.134 |
| min | 1.000 | 1.000 | 0.002 | 0.002 | 0.398 | 0.366 |
| 25% | 1.070 | 1.079 | 0.118 | 0.114 | 1.533 | 1.571 |
| 50% | 1.479 | 1.473 | 0.650 | 0.624 | 4.019 | 3.790 |
| 75% | 1.711 | 1.702 | 2.320 | 2.132 | 6.754 | 6.321 |
| max | 2.041 | 2.034 | 10.864 | 10.954 | 13.834 | 14.090 |
DataSet DType Table Chunk_Size 1051380 /s501/events.integrals int32 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 |
| mean | 2.199 | 2.196 | 1.574 | 1.473 | 4.854 | 4.707 |
| std | 0.478 | 0.465 | 1.848 | 1.672 | 3.330 | 3.265 |
| min | 1.000 | 1.000 | 0.003 | 0.003 | 0.503 | 0.363 |
| 25% | 1.979 | 1.983 | 0.194 | 0.184 | 2.044 | 1.979 |
| 50% | 2.197 | 2.184 | 0.935 | 0.899 | 4.277 | 4.094 |
| 75% | 2.555 | 2.538 | 2.265 | 2.120 | 6.912 | 6.646 |
| max | 3.337 | 3.353 | 10.366 | 7.975 | 14.669 | 14.785 |
DataSet DType Table Chunk_Size 1077300 /s503/events.pulseheights int16 2.000 7.706
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 |
| mean | 1.476 | 1.609 | 1.685 | 1.849 | 5.506 | 5.006 |
| std | 0.364 | 0.407 | 2.447 | 2.514 | 4.916 | 4.439 |
| min | 1.000 | 1.000 | 0.003 | 0.003 | 0.463 | 0.445 |
| 25% | 1.085 | 1.216 | 0.117 | 0.131 | 1.623 | 1.594 |
| 50% | 1.517 | 1.733 | 0.697 | 0.763 | 3.803 | 3.878 |
| 75% | 1.811 | 1.954 | 2.334 | 2.602 | 6.985 | 6.048 |
| max | 2.157 | 2.304 | 10.535 | 10.696 | 15.687 | 15.782 |
DataSet DType Table Chunk_Size 1080540 /s503/events.integrals int32 2.000 15.412
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 |
| mean | 2.322 | 2.681 | 1.632 | 2.165 | 4.971 | 5.347 |
| std | 0.570 | 0.561 | 1.851 | 2.453 | 3.185 | 3.381 |
| min | 1.000 | 1.309 | 0.004 | 0.006 | 0.644 | 0.652 |
| 25% | 1.984 | 2.540 | 0.204 | 0.266 | 2.411 | 2.482 |
| 50% | 2.245 | 2.748 | 1.010 | 1.200 | 4.540 | 5.224 |
| 75% | 2.684 | 3.026 | 2.361 | 3.383 | 7.110 | 7.711 |
| max | 3.470 | 3.780 | 9.585 | 12.086 | 13.480 | 12.727 |
DataSet DType Table Chunk_Size 1104840 /s506/events.pulseheights int16 2.000 14.449
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 | 1,620.000 |
| mean | 1.414 | 1.410 | 1.946 | 1.703 | 5.586 | 5.287 |
| std | 0.315 | 0.308 | 2.804 | 2.389 | 4.372 | 4.332 |
| min | 1.000 | 1.000 | 0.002 | 0.003 | 0.481 | 0.409 |
| 25% | 1.069 | 1.083 | 0.133 | 0.126 | 1.838 | 1.702 |
| 50% | 1.487 | 1.479 | 0.722 | 0.683 | 4.954 | 4.501 |
| 75% | 1.702 | 1.696 | 2.572 | 2.316 | 7.118 | 6.470 |
| max | 2.040 | 2.034 | 11.275 | 11.068 | 13.450 | 13.589 |
DataSet DType Table Chunk_Size 1109700 /s506/events.integrals int32 2.000 16.000
| N_CRate | COL_CRate | N_CSpeed | COL_CSpeed | N_DSpeed | COL_DSpeed | |
|---|---|---|---|---|---|---|
| count | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 | 3,240.000 |
| mean | 2.204 | 2.203 | 1.673 | 1.551 | 5.080 | 4.912 |
| std | 0.473 | 0.462 | 1.915 | 1.721 | 3.341 | 3.268 |
| min | 1.000 | 1.000 | 0.004 | 0.005 | 0.592 | 0.558 |
| 25% | 1.985 | 1.987 | 0.213 | 0.205 | 2.255 | 2.286 |
| 50% | 2.213 | 2.202 | 0.969 | 0.936 | 4.682 | 4.568 |
| 75% | 2.545 | 2.544 | 2.388 | 2.253 | 7.217 | 6.879 |
| max | 3.330 | 3.390 | 10.567 | 8.706 | 13.782 | 13.920 |
Como era de esperar, parece que las tablas columnares son más comprimibles. Aunque hay casos en los que se comprimen igual, nunca se comprimen menos.
Para poder visualizar todas estas correlaciones calculamos directamente el coeficiente de pearson asociado entre los datos de blosclz con nivel de compresión 1 y el resto.
cst.paint_codec_pearson_corr(my_df, 'blosclz', 1)
Aquí hacemos los mismo para LZ4
cst.paint_codec_pearson_corr(my_df, 'lz4', 1)
Los resultados son bastante buenos, además era de esperar. Aunque LZ4 tiene mejores resultados ambas opciones parecen lo suficientemente buenas.
Aquí se trata de observar las correlaciones entre características de chunk seleccionadas y las pruebas de compresiones. Para ello se utiliza un gráfico de pares personalizado. Además los datos se filtran por codec, filtro, nivel de compresión y tamaño de bloque, sino no tendría sentido los gráficos debido a la enorme variabilidad que habría.
dfaux = my_df[(my_df.Codec == 'lz4') & (my_df.Block_Size == 256) &
(my_df.Filter == 'shuffle') & (my_df.CL == 5) &
(my_df.DType.str.contains('float') |
my_df.DType.str.contains('int'))]
cols = ['Mean', 'Sd', 'Skew', 'Kurt']
cst.custom_pairs(dfaux, cols)
680 points
cols = ['Range', 'Q_Range', 'N_Streaks']
dfaux = dfaux.assign(Range=dfaux['Max'] - dfaux['Min'])
dfaux = dfaux.assign(Q_Range=dfaux['Q3'] - dfaux['Q1'])
cst.custom_pairs(dfaux, cols)
680 points
Aunque se podría plantear decir que a mayor rango y número de rachas disminuye el ratio de compresión, no sería muy adecuado sacar conclusiones de estos gráficos. Hay demasiada variabilidad en los datos en sí como para extraer conclusiones de un simple gráfico, será mejor que estas correlaciones las busquen los algoritmos de clasificación en sí.